Flood Frequency of Unregulated Streams of Tennessee Using Regional-Regression Equations and the Region-of-Influence Method

摘要

Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include regional-regression equations and the newer region-of-influence method. The prediction methods were developed using streamgage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. However, these methods should not be used in heavily developed or storm-sewered basins with hydraulically-effective impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged streams of Tennessee. rnRegional-regression methods were derived by using both single-variable and multivariable regression analysis. Contributing drainage area is the explanatory variable used in the singlevariable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Average deleted-residual prediction errors for the single-variable equations ranged from 32 to 65 percent. Average deleted-residual prediction errors for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. rnThe region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar gages selected from the study area. Explanatory variables that may be used in prediction equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Average deleted-residual prediction errors for the region-of-influence method tended to be slightly smaller than those for the regional-regression methods and ranged from 27 to 62 percent.